To overcome this problem, this study proposes a hierarchical Dirichlet process–variational autoencoder–Gaussian process–hidden semi-Markov model (HVGH). The parameters of the proposed HVGH are estimated through a mutual learning loop of the variational autoencoder and our previously proposed HDP-GP-HSMM.

2019年11月20日 · Humans perceive continuous high-dimensional information by dividing it into meaningful segments, such as words and units of motion. We believe that such unsupervised segmentation is also important for robots to learn topics such as language and motion.

To overcome this problem, this study proposes a hierarchical Dirichlet process–variational autoencoder–Gaussian process–hidden semi-Markov model (HVGH). The parameters of the proposed HVGH are estimated through a mutual learning loop of the variational autoencoder and our previously proposed HDP-GP-HSMM.

To overcome this problem, this study proposes a hierarchical Dirichlet process–variational autoencoder–Gaussian process–hidden semi-Markov model (HVGH). The parameters of the proposed HVGH are...

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... the model we propose in this paper is a hierarchical Dirichlet process-variational autoencoder-Gaussian processhidden semi-Markov model (HVGH 1 ). Figure 1 shows an overview of HVGH.

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HIERARCHICAL DIRICHLET PROCESS-VARIATIONAL AUTOENCODER-GAUSSIAN PROCESS-HIDDEN SEMI-MARKOV MODEL (HVGH) Figure 3 shows a graphical model of our proposed HVGH, which is a generative model of...

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